From 696935148ab2a827b530bc679be8ff67430b6a44 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Wed, 27 Oct 2021 09:22:37 +0200
Subject: [PATCH] name of pdf starts now with feature_importance, there is now
also another separated vars plot for single input feature importance
---
mlair/plotting/postprocessing_plotting.py | 38 ++++++++++++++++-------
1 file changed, 27 insertions(+), 11 deletions(-)
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index d9551815..92a327a6 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -622,7 +622,7 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
:param bootstrap_annotation: additional information to use in the file name (default: None)
"""
annotation = ["_".join([s for s in ["", bootstrap_type, bootstrap_method] if s is not None])][0]
- super().__init__(plot_folder, f"skill_score_bootstrap_{model_setup}{annotation}")
+ super().__init__(plot_folder, f"feature_importance_{model_setup}{annotation}")
if separate_vars is None:
separate_vars = ['o3']
self._labels = None
@@ -645,6 +645,10 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
self._plot(branch=branch)
self.plot_name = f"{plot_name}_{branch}"
self._save()
+ if len(set(separate_vars).intersection(self._data[self._x_name].unique())) > 0:
+ self.plot_name += '_separated'
+ self._plot(branch=branch, separate_vars=separate_vars)
+ self._save(bbox_inches='tight')
else:
self._plot()
self._save()
@@ -673,6 +677,7 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
"""
station_dim = "station"
data = helpers.dict_to_xarray(data, station_dim).sortby(self._x_name)
+ data = data.transpose(station_dim, self._ahead_dim, self._boot_dim, self._x_name)
if self._boot_type == "single input":
number_tags = self._get_number_tag(data.coords[self._x_name].values, split_by='_')
new_boot_coords = self._return_vars_without_number_tag(data.coords[self._x_name].values, split_by='_',
@@ -741,10 +746,10 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
if separate_vars is None:
self._plot_all_variables(branch)
else:
- self._plot_selected_variables(separate_vars)
+ self._plot_selected_variables(separate_vars, branch)
- def _plot_selected_variables(self, separate_vars: List):
- data = self._data
+ def _plot_selected_variables(self, separate_vars: List, branch=None):
+ data = self._data if branch is None else self._data[self._data["branch"] == str(branch)]
self.raise_error_if_separate_vars_do_not_exist(data, separate_vars, self._x_name)
all_variables = self._get_unique_values_from_column_of_df(data, self._x_name)
remaining_vars = helpers.remove_items(all_variables, separate_vars)
@@ -761,14 +766,21 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=data_first, ax=ax[0], whis=1.5,
palette="Blues_d", showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
- flierprops={"marker": "."}, width=first_box_width)
+ showfliers=False, width=first_box_width)
ax[0].set(ylabel=f"skill score", xlabel="")
+ if self._ylim is not None:
+ _ylim = self._ylim if isinstance(self._ylim, tuple) else self._ylim[0]
+ ax[0].set(ylim=_ylim)
sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=data_second, ax=ax[1], whis=1.5,
palette="Blues_d", showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
- flierprops={"marker": "."})
+ showfliers=False)
ax[1].set(ylabel="", xlabel="")
ax[1].yaxis.tick_right()
+ if self._ylim is not None and isinstance(self._ylim, list):
+ _ylim = self._ylim[1]
+ ax[1].set(ylim=_ylim)
+
handles, _ = ax[1].get_legend_handles_labels()
for sax in ax:
matplotlib.pyplot.sca(sax)
@@ -835,17 +847,21 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
plot_data = self._data if branch is None else self._data[self._data["branch"] == str(branch)]
if self._boot_type == "branch":
fig, ax = plt.subplots(figsize=(0.5 + 2 / len(plot_data[self._x_name].unique()) + len(plot_data[self._x_name].unique()),4))
- sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=plot_data, ax=ax, whis=1., palette="Blues_d",
- showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."},
- width=0.8)
+ sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=plot_data, ax=ax, whis=1.,
+ palette="Blues_d", showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
+ showfliers=False, width=0.8)
else:
fig, ax = plt.subplots()
sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=plot_data, ax=ax, whis=1.5, palette="Blues_d",
- showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."})
+ showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, showfliers=False)
ax.axhline(y=0, color="grey", linewidth=.5)
if self._ylim is not None:
- ax.set(ylim=self._ylim)
+ if isinstance(self._ylim, tuple):
+ _ylim = self._ylim
+ else:
+ _ylim = (min(self._ylim[0][0], self._ylim[1][0]), max(self._ylim[0][1], self._ylim[1][1]))
+ ax.set(ylim=_ylim)
if self._boot_type == "branch":
plt.xticks()
--
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